Title
Attacking Randomized Exponentiations Using Unsupervised Learning
Abstract
Countermeasures to defeat most of side-channel attacks on exponentiations are based on randomization of processed data. The exponent and the message blinding are particular techniques to thwart simple, collisions, differential and correlation analyses. Attacks based on a single (trace) execution of exponentiations, like horizontal correlation analysis and profiled template attacks, have shown to be efficient against most of popular countermeasures. In this paper we show how an unsupervised learning can explore the remaining leakages caused by conditional control tests and memory addressing in a RNS-based implementation of the RSA. The device under attack is protected with the exponent blinding and the leak resistant arithmetic. The developed attack combines the leakage of several samples over the segments of the exponentiation in order to recover the entire exponent. We demonstrate how to find the points of interest using trace pre-processing and clustering algorithms. This attack can recover the exponent using a single trace.
Year
DOI
Venue
2014
10.1007/978-3-319-10175-0_11
CONSTRUCTIVE SIDE-CHANNEL ANALYSIS AND SECURE DESIGN
Keywords
Field
DocType
RSA, Randomized exponentiation, Electromagnetic analysis, Unsupervised learning, Clustering algorithms, Single-execution attacks
Exponent,Blinding,Computer science,Algorithm,Correlation,Unsupervised learning,Memory address,Point of interest,Cluster analysis,Exponentiation
Conference
Volume
ISSN
Citations 
8622
0302-9743
5
PageRank 
References 
Authors
0.49
15
4
Name
Order
Citations
PageRank
Guilherme Perin1329.03
Laurent Imbert221718.69
Lionel Torres350.49
Philippe Maurine427640.44